• Structural Engineering and Mechanics
• /
• v.32 no.1
• /
• pp.21-36
• /
• 2009

An importance sampling method is presented for computing the first passage probability of elasto-plastic structures under stochastic excitations. The importance sampling distribution corresponds to shifting the mean of the excitation to an 'adapted' stochastic process whose future is determined based on information only up to the present. A stochastic control approach is adopted for designing the adapted process. The optimal control law is determined by a control potential, which satisfies the Bellman's equation, a nonlinear partial differential equation on the response state-space. Numerical results for a single-degree-of freedom elasto-plastic structure shows that the proposed method leads to significant improvement in variance reduction over importance sampling using design points reported recently.

• Nuclear Engineering and Technology
• /
• v.47 no.5
• /
• pp.546-558
• /
• 2015

Chord length sampling method in Monte Carlo simulations is a method used to model spherical particles with random sampling technique in a stochastic media. It has received attention due to the high calculation efficiency as well as user convenience; however, a technical issue regarding boundary effect has been noted. In this study, after analyzing the distribution characteristics of spherical particles using an explicit method, an alternative chord length sampling method is proposed. In addition, for modeling in finite media, a correction method of the boundary effect is proposed. Using the proposed method, sample probability distributions and relative errors were estimated and compared with those calculated by the explicit method. The results show that the reconstruction ability and modeling accuracy of the particle probability distribution with the proposed method were considerably high. Also, from the local packing fraction results, the proposed method can successfully solve the boundary effect problem. It is expected that the proposed method can contribute to the increasing of the modeling accuracy in stochastic media.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 1999.10a
• /
• pp.351-358
• /
• 1999

The structural responses of underground structures are examined in probability by using the elasto-plastic stochastic finite element method in which the spatial distributions of material properties are assumed to be stochastic fields. In addition, the adaptive importance sampling method using the response surface technique is used to improve simulation efficiency. The method is found to provide appropriate information although the nonlinear Limit State involves a large number of basic random variables and the failure probability is small. The probability of plastic local failures around an excavated area is effectively evaluated and the reliability for the limit displacement of the ground is investigated. It is demonstrated that the adaptive importance sampling method can be very efficiently used to evaluate the reliability of a large scale stochastic finite element model, such as the underground structures located in the multi-layered ground.

• International Journal of Control, Automation, and Systems
• /
• v.5 no.5
• /
• pp.515-525
• /
• 2007

In this paper, we develop a co-design methodology of stochastic optimal controllers and network parameters that optimizes the overall quality of control (QoC) in networked control systems (NCSs). A new dynamic model for NCSs is provided. The relationship between the system stability and performance and the sampling frequency is investigated, and the analysis of co-design of control and network parameters is presented to determine the working range of the sampling frequency in an NCS. This optimal sampling frequency range is derived based on the system dynamics and the network characteristics such as data rate, time-delay upper bound, data-packet size, and device processing time. With the optimal sampling frequency, stochastic optimal controllers are designed to improve the overall QoC in an NCS. This co-design methodology is a useful rule of thumb to choose the network and control parameters for NCS implementation. The feasibility and effectiveness of this co-design methodology is verified experimentally by our NCS test bed, a ball magnetic-levitation (maglev) system.

• The Korean Journal of Applied Statistics
• /
• v.28 no.2
• /
• pp.175-188
• /
• 2015

This study is about estimation methods for the population pharmacokinetic and pharmacodymic model. This is a nonlinear mixed effect model, and it is difficult to find estimates of parameters because of nonlinearity. In this study, we examined theoretical background of various estimation methods provided by NONMEM, which is the most widely used software in the pharmacometrics area. We focused on estimation methods using a stochastic sampling approach - IMP, IMPMAP, SAEM and BAYES. The SAEM method showed the best performance among methods, and IMPMAP and BAYES methods showed slightly less performance than SAEM. The major obstacle to a stochastic sampling approach is the running time to find solution. We propose new approach to find more precise initial values using an ITS method to shorten the running time.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2004.05a
• /
• pp.685-688
• /
• 2004

본 연구에서는 저수지군 연계운영을 위한 표본 추계학적 선형 계획(SSLP, Sampling Stochastic Linear Programming) 모형을 제안한다. 일반적 추계학적 모형은 과거 자료로부터 확률변수의 확률분포를 추정하고 이를 몇 개 구간으로 나누어 이산 확률 값을 산정하여 기댓값이 최대가 되는 운영방안을 도출하지만 저수지 유입량 예측시 고려되어야할 지속성 효과(Persistemcy Effect)와 유역간 또는 시점별 공분산 효과(The joint spatial and temporal correlations)를 반영하는데 많은 한계가 있다. 이를 극복하기 위하여 과거자료 자체를 유입량 시나리오로 적용하여 시${\cdot}$공간적 상관관계를 유지하는 표본 추계학적(Sampling Stochastic)기법을 바탕으로 Simple Recourse Model로 구성한 추계학적 선형 계획 모형을 제시한다. 이 모형은 미국 기상청(NWS)에서 발생 가능한 유입량의 시나리오를 예측하는 방법인 앙상블 유량 예측(ESP, Ensemble Streamflow Prediction)을 통한 시나리오를 적용함으로써 좀더 신뢰성 있는 저수지군 연계운영 계획을 도출 할 수 있을 것으로 기대된다.

• Nuclear Engineering and Technology
• /
• v.54 no.11
• /
• pp.4272-4279
• /
• 2022

In this study, a cross section stochastic sampling (S.S.) capability is implemented into both the McCARD continuous energy Monte Carlo code and MIG multiple-correlated data sampling code. The ENDF/B-VII.1 covariance data based 30 group cross section sets and the SCALE6 covariance data based 44 group cross section sets are sampled by the MIG code. Through various uncertainty quantification (UQ) benchmark calculations, the McCARD/MIG results are verified to be consistent with the McCARD stand-alone sensitivity/uncertainty (S/U) results and the XSUSA S.S. results. UQ analyses for Three Mile Island Unit 1, Peach Bottom Unit 2, and Kozloduy-6 fuel pin problems are conducted to provide the uncertainties of k_eff and microscopic and macroscopic cross sections by the McCARD/MIG code system. Moreover, the SNU S/U formulations for uncertainty propagation in a MC depletion analysis are validated through a comparison with the McCARD/MIG S.S. results for the UAM Exercise I-1b burnup benchmark. It is therefore concluded that the SNU formulation based on the S/U method has the capability to accurately estimate the uncertainty propagation in a MC depletion analysis.

• Geomechanics and Engineering
• /
• v.7 no.5
• /
• pp.525-537
• /
• 2014

The typical design of ground improvement with prefabricated vertical drains (PVD) and surcharge preloading involves a series of deterministic analyses using averaged or mean soil properties for the various combination of the PVD spacing and surcharge preloading height that would meet the criteria for minimum consolidation time and required degree of consolidation. The optimum design combination is then selected in which the total cost of ground improvement is a minimum. Considering the variability and uncertainties of the soil consolidation parameters, as well as considering the effects of soil disturbance (smear zone) and drain resistance in the analysis, this study presents a stochastic cost optimization of ground improvement with PVD and surcharge preloading. Direct Monte Carlo (MC) simulation and importance sampling (IS) technique is used in the stochastic analysis by limiting the sampled random soil parameters within the range from a minimum to maximum value while considering their statistical distribution. The method has been verified in a case study of PVD improved ground with preloading, in which average results of the stochastic analysis showed a good agreement with field monitoring data.

• Proceedings of the IEEK Conference
• /
• 1998.06a
• /
• pp.46-49
• /
• 1998

In this paper, we introduce the notion of intelligent communication by introducing a novel intelligent receiver model. This receiver is continually evolving and learns and improves in performance as it compiles its experience over time. In digital communication context, in a typical training mode, it jearns the concept of "1" as is deteriorated by arbitrary (not necessarily additive as is typically assumed) disturbance and /or modulation. After learning "1", in test mode, it classifies the received signal "1" and "0" almost completely. The intelligent receiver as implemented is grounded on the recently introduced Stochastic Morphological Sampling Theorem(SMST), a distribution-free result which gives theoretical bounds on the sample complexity(training size) needed for the required performance parameters such as accuracy($\varepsilon$) and confidence($\delta$). Based on this theorem, we demonstrate --almost irrespective of channel and modulation model-- the number of samples needed to learn the concept of "1" is not too "large" and the resulting universal receiver structure, that corresponding to classical Nearest Neighbor rule in Pattern Recognition Theory, is trivial. We check the surprising efficiency and validity of this model through some simple simulations. and validity of this model through some simple simulations.

• Journal of the Korean Statistical Society
• /
• v.32 no.2
• /
• pp.103-119
• /
• 2003

Properties of stochastic regression imputation are discussed under the uniform within-cell response model. Variance estimator is proposed and its asymptotic properties are discussed. A limited simulation is also presented.